Topic document attached.- Capstone Project Organizing of the paper-Outline: Data Analysis 1. Introduction• General Background about the data analysis method.• e.g., This chapter presents the results of data collection and data analysis performed by this research study. The main goal or purpose of this study is to discover and identify issues which….A Google survey form was implemented, and various senior cybersecurity experts/students/ participated in the survey.• This chapter also contains the results of the study conducted to answer the following research questions: 2. Data Collection• How?• E.g., Data collection occurred between Oct 2021 and December 2021. The participants in the survey were….Google Forms was used to present the survey instrument to the participants. A total of xx responses were received, from which % responses were usable. A qualitative data analysis software were used. Once the data was collected from Google Forms, it was formatted in Microsoft Excel (.csv) and imported to [whatever tool you are using].3. Data Analysis (You may also add here Demographics Analysis)• e.g. Proper data analysis is instrumental in identifying and aligning the various variables so that a valuable and comprehensive final output can be deduced. Data analysis is a procedure that is one of the most crucial parts of any research work. This study therefore employed a grounded theory data analysis approach to identify and categorize feedbacks from the research participants.4. Findings• Here you must clearly identify your findings in graphs/charts and other visualization tools.• e.g., Participants were asked if …………. And xx% responded that…whereas the remaining xxx%5. Summary• A summary of Chapter Four in a paragraph or two.• e.g., The results showed that most of the organizations in this study do not have……..6. References1-2 pages needed.
Topic document attached.- Capstone Project Organizing of the paper-Outline: Data Analysis 1. Introduction • General Background about the data analysis method. • e.g., This chapter pres
23 Impact of Data Loss and Restoration Chapter One Introduction Overview Today, many firms that conduct business electronically are dealing with information security challenges, particularly data breaches. The high frequency of security breaches, along with significant financial losses, has caused organizations to be wary of investing in information technology (IT) (Chang et al., 2020). Data breach, often known as data loss, refers to the misplacing or loss of important company files and documents. Many of businesses lose data due to deletion and due to the presence of corrupted viruses in the system. While this has become an issue, some of the data that has been lost can be recovered by IT experts and specialists. Viruses and malware malicious software placed in the system to corrupt files is two significant variables contributing to data loss. Second, data loss can occur due to human error, such as accidentally deleting a file and hacking activities, hard drive damage, insider threats, and ransomware (Cheng et al., 2017). As a result, data restoration and recovery solutions have been developed to restore lost data from another useable copy. Data erased due to human error can now be easily recovered using recovery tools embedded in the systems (A, 2018). To combat this issue, many restoration technologies have been used. As a result, it’s critical to assess the restore technique and the data recovery tools used to ensure that a trustworthy data backup version is accessible for restoration. Protection copies should be reviewed at random intervals to ensure that they meet recovery point objectives (RPOs). The data being restored must be legible, consistent with a given time period, and have the necessary information to meet RPOs (Posey & Peterson, 2020). Even with businesses’ attention to data loss and restoration, incidences of data loss still occur. Historically, public disclosure of data breaches began in the 1980s, but it became more common in the early 2000s. The first data breach occurred in 1984 when TRW, global credit information corporation, was hacked, and around 90 million records were stolen (Sobers, 2021). A similar incident occurred in 1986 when 16 million records from Revenue Canada were taken. However, in 2005, a new pattern of data breaches emerged. As individuals become more reliant on computers for transactions, shopping, and other activities, a single cyber-attack on a firm can have massive impact consumers. A data leak in the healthcare industry has affected around 249.09 people by the year 2019 (Seh et al., 2020). There were 2216 data breaches reported in 65 countries around the world in 2018. This research investigates the cause of data loss, prevention measures, and efficient data restoration techniques that can be applied in an organizational setting once a firm experience data loss (Seh et al., 2020). This research looks into certain gaps. First, the proposed data restoration technique and tools face challenges in backing up and recovery data. Although studies have been conducted to evaluate data loss and data restoration techniques, no studies have been conducted to propose practical solutions for safer backup procedures. Previous research did not consider the follow-up techniques for data restoration and the steps to be taken once data is lost (Tessian, 2021). This will be taken into account in the research while attempting to identify recovery procedures. The following section will elaborate data loss problems by explaining how things should function, why the problem is important, supporting claims, possible solutions, and the solution’s benefits. The next sections will include the problem purpose, research question, study importance, limitation, definition of terms and organization of the remaining chapters. Problem Statement A strong data protection plan is one of the most important data security procedures for securing a company’s assets. Robust data security measures can help prevent data breaches and leaks caused by external influence or internal employee interaction. One of the most significant advantages of an efficient data protection protocol is the protection of a recognized firm’s reputation as one of the key challenge modern business faces (Wheatley & et al., 2019). The goal of a data protection plan is to prevent breaches, leakage, loss, or corruption. Data protection entails two components: data accessibility and data management. The data accessibility method comprises instant access and use of data required to do business, even if the data is distorted or deleted. On the other hand, data management is the process of securely shifting vital data between online and offline storage. This method secures data from errors, corruption, breaches and attacks, hardware or software failure, and natural disasters. The reason for selecting this topic is that there has been a significant increase in data manipulation by hackers who damage files with dangerous software to extort or gain money. Many organizations spend large sums of money retrieving lost data and even managing their data files, which disrupts their day-to-day operations and results in losses (Wheatley & et al., 2019). Data breaches have both long and short-term consequences for businesses. The short-term cost of a data breach includes missed sales and the materials and personnel required for data recovery, all of which may be assessed. Another short-term cost is a loss of investor trust in the company, which negatively impacts firms’ stock performance and price (Cheng et al., 2017). On the other hand, the long-term expenses are difficult to quantify because it is difficult to evaluate the loss of customer trust and the erosion of the organization’s reputation. According to Romanosky (2016, Wang et al., 2019), the average cost of a cyber-event is less than $200,000, which is less than the cost of the company’s annual sales. The legal cost is 19%, the customer compensation cost is 18%, the third party remedial resources cost is 15%, the fine or compliance cost is 15%, and the public relations compensation cost is roughly 18% (Wang et al., 2019). Modern data system has some extra data layers on top of the usual operating and file system, which creates some issues when retrieving data. To get the old file system, the experts must reconstruct the logic on the system. This is a common issue with high-end enterprise products that determine where to store each file part (Marsaid et al., 2019). Creating proactive data security measures is crucial in combating data breaches. Proactive security measures include developing security awareness training, penetration testing, proactive endpoint, network monitoring, threat hunting, and threat intelligence (Angelini & et al., 2015). Reactive cyber security is a comprehensive approach to cyber security that focuses on prevention rather than detection and response. It enables the organization to understand the areas of vulnerability better and minimize them. Reactive measures for cyber security risks are procedures put in place to prevent any attack before it occurs. The reactive measures include cyber security monitoring systems, forensic analysis of security events, anti-spam/anti-malware solution, and firewall (Angelini et al., 2015). The best method to keep data and networks safe and secure is to use a combination of reactive and proactive measures. Another method that may be used to solve backup and recovery speed is to eliminate backup windows and minimize recovery timeframes (Angelini & et al., 2015). Data storage in the cloud makes financial sense since it eliminates the firm capital and operational costs involved with procuring new infrastructure. SaaS and cloud-native applications are two cloud options that can be used to store data. Symantec backup exec 2014 and Netbackup 7.6 can aid in the elimination of backup and recovery windows. The recommended solutions are advantageous since they would reduce the amount of time cyber security experts spend recovering data from damaged hard disk. Cloud computing will ensure data is easily accessible via the cloud. Addressing cyber security threats through reactive and proactive methods ensures that vulnerability concerns on the firm system are recognized, suspicious behavior is identified, compliance is increased, and cyber security threats are prevented (Chang et al., 2020). Cyber security measures prevent data loss caused by employee error, hackers, or human error. In conclusion, data loss hazards caused by human error, cyber activity, or insider work severely damage an organization’s reputation and increased financial loss to the firm and customers (Chang et al., 2020). The time a company spends recovering data still consumes a considerable amount of productive time. Proactive and reactive cyber security measures and automatic data recovery and storage in the cloud reduce data restoration complications and the potential of the data breach. This is due to the fact that data security threats and vulnerabilities will be recognized and avoided before they harm the company’s IT system. Statement of Purpose The main purpose of study is to discover and identify issues which make organization vulnerable to data loss. Qualitative research will be used to investigates the interaction between proactive and reactive cyber security measures, automatic data recovery, and cloud computing and their impact on data loss prevention and efficient data restoration in the workplace. The survey, questionnaire questions will be disseminated through emails using a pro-email management tool to deliver and collect surveys. The people interviewed include data security managers in the working setting. The research will focus on collecting data on causes of data loss, prevention measures critical in preventing data loss, and appropriate data restoration techniques that would be effective in restoring data lost through hackers, damaged hard disks, and delete data by mistake and insider threats. The study will encompass 20 selected organizations’ data security managers in the retail, banking sector, and retail setting in the U.S. The personnel will respond to the interview, questionnaire, and survey questions on proactive and reactive measures, data recovery and restoration, and data loss cause. Research Questions The main research question that this study will examine is what preventative methods the company may implement to avoid data loss and is there an easier way to recover data. The following are examples of potential research questions; however, they are not limited. Research Q1: Is there a statistically significant relationship between proactive and reactive cyber security measures, cloud computing, and automatic data recovery on preventing data loss and promoting efficient data restoration? Research Q2: Is there a significant relationship between regular risk assessment, staff training, updated software, third-party data security evaluation, and cloud computing in promoting effective data restoration and preventing data loss? Research Q3.: Is there an important relationship between encryption, regular update of the system, cloud computing, and their impact on preventing data loss and efficient data restoration? Significance This study is relevant as it will provide a thorough insight into the data backup and restoration, which has limited research that gives extensive information on what happens after the data is recovered and challenges that are faced during data restoration activity in the modern workplace setting. Furthermore, the study would be crucial in providing significant information relating to data prevention measures employed in the modern workplace and how modern organizations are actively assessing their system to detect vulnerability and measures they put in place. The information acquired will be crucial in expanding the body of knowledge on how automatic data recovery, cloud computing, and reactive and proactive methods will be used to prevent data loss and restore data efficiently in the workplace. Limitation The limitation of this study includes issues with small sample size of 20 data security managers. Statistical tests would fail to detect a meaningful link between data sets due to sample size used. A large sample size may yield accurate results. Time constraints and access to research participants is another concern that limited the research. The study depends on the access to data security managers for selected organizations and information on data security breaches, but some organizations denied access to certain information or comment, and some staffs were inaccessible due to busy work schedules. The time allocated to investigate the research problem and to access the independent and dependent variables was limited due to assignment due time. Definition of Terms Data Loss: Data loss is defined as a technique or event that causes data to be damaged, destroyed, or rendered inaccessible by a user, program, or application. Malware-Malicious Software: is any application or document that poses a risk to a user. Hard Drive Damages: When a system cannot successfully complete data to a file or when sections of the file become unreadable, hard disk data leakage occurs. It is a prevalent source of data loss since damaged files are frequently inaccessible. Insider Threat: An insider threat is a potential threat that arises within the company being attacked. Ransomware: Ransomware is a form of virus that prohibits or restricts users’ accessibility to their machine, either by locking the system’s screen or by encrypting the users’ documents until a ransom has been paid. Recovery Point Objective (RPO): The volume of data which can be lost before severe damage happens between the time of a critical incident as well as the most recent backup is referred to as the Recovery Point Objective (RPO). Cloud Computing: A concept for providing ubiquitous, easy, on-demand access to a common pool of customizable computing resources that can be swiftly provided and delivered with no administrative effort or involvement from service providers. Software as a Service (SaaS): It is a software distribution method in which a cloud provider hosts programs and make them accessible to end customers through the internet. Organization of the Remaining Chapter The study will be presented in five chapters. Chapter 2 will provide a Review of Selected Literature. Chapter 3 then covers the description of the Completed Project which is Methodology. We are going to cover Data Analysis Methods in Chapter 4. Finally, the result of the research study will be provided in Chapter 5. Chapter Two Literature Review Introduction Data loss is an event where data is destroyed, deleted, corrupted, or made incomprehensible by operators and software applications. A data loss incident can be intentional or accidental (Palkó & Sujbert, 2021). Data loss results in some or all of the information becoming useless by the owner or its conforming software application. Data can be due to theft, computer virus, hardware impairment, power failure, or software corruption. The theft or loss of a device comprising data is reflected as part of data loss. Data restoration is the process of recovering data from any storage media after data loss. Data restoration incorporates a set of procedures used to recover lost information. The high occurrence of data loss experienced by technology users suggests it would be wise to reflect on your data recovery choices before trouble starts. Data loss is one of the most serious issues for organizations. Conceptual Framework Data loss is one of the most serious issues for organizations. Past research on data loss has essentially centered around two focuses; striving to reduce the chances of a data loss by addressing every employee by monitoring their behavior and considering the effects of data loss on organizations. Data loss has been one of the most ordinary worries for any business association that arrangements with general society. From the outcome, the idea of data loss has been changing gradually. One alarming outcome that had been gotten in our end study is, the data loss is straightforwardly ascribed to the implementation of safety arrangements that accounts for critical jobs. Eventually, the organizations should carry out rigid security arrangements and give vital and adequate preparation to authorize these strategies on the workers. Data Systems (IS) is the Centre space where associations should lead their business procedures and work with their partners (ONU & AKIENE). It is the fundamental responsibility of the organization to safeguard the and accessibility, trustworthiness, and privacy of the Information frameworks to work appropriately to have the confidence of partners towards the organization. It had an immense effect on IS because of the expanded number of data losses. Hence, the issues related to data security and secrecy have turned into the greatest concerns for businesses and supervisors. For any organization, information violations are huge difficulties. Any unapproved access or inadvertent disclosure of delicate data brings about unfriendly results to the business’s reputation. In this manner, the business entity can be additionally forced fines because of administrative consistency and may even have lawful activity from clients, which thus expands the consumption for further developing security and may even prompt loss of client trust. The new study on data loss expresses that loss of information increases the expenses to the ordinary hierarchical expense of an organization’s data (Taylor, 2021). In this way, scientists need to research these issues and begin to analyze the issues identified with data loss. Historical Background Data loss has been happening since computer systems initially began showing up in working environments and started to notably rise during the 1980s. For instance, in 1984, the organization presently known as Experian had 90 million records lost in an attack. In 1986, there was another occurrence bringing about 16 million records being taken from Revenue Canada. Into the ’90s and mid-2000s, the quantity of data loss kept on expanding; however, so did public awareness (Hsia, 2021). Cyber theft turned into a generally normal media feature, and individuals and organizations started pushing for more grounded information protection arrangements. During this time, legal elements likewise started to pay attention. For instance, in 2003, California passed the primary law securing buyers’ very own data protection. This law provoked numerous previously negligent associations regarding their approaches or lacking them completely to pay attention to data loss and start focusing on defensive measures. Regardless of this abrupt development of consideration, starting in 2012, states had laws managing how to deal with data loss. On the other hand, data restoration became effective in the 1970s due to the rise of technologies. Before this time, many businesses stored paper records that could be damaged by fire as well as theft. In 1980, the government of the United States ordered every bank to have a testable backup for their documents. In 1990, the three-tier architecture was used to separate data from the user interface and application layer (Hsia, 2021). Data recovery had evolved until the 2010s when cloud computing was discovered, and it has reduced disastrous loss of data as well as reducing the cost of data recovery. Prior Studies This study aims to see how data loss happens and its impacts, with the end goal that data loss can be easily stopped. Talesh (2018) stated that employees are the primary reason behind the cause of majority of the data loss. Their non-compliance with the rules of safety administered by the business organization is the principal reason. According to a study done by Zeno-Zencovich (2018), organizations are to forced intensely by fines when they track down that private information loss is informed. The most effective solution to data loss is a good data backup system, as it upsurges the probabilities of data restoration. Data restoration is mostly done by specialized firms that inspect hardware storage to recover deleted information and the effort to restore corrupted data through specialized procedures. For example, ACE Data Recovery has been outshining since 1981, recuperating data from RAID arrays, hard drives, SSDs, and server data. What makes this agency exclusive is its capability to design and construct its hardware and software entirely for data restoration. Research Gaps Virus and formatting errors are the main causes of data loss from their storage. There is the need to put in place strategies that would help prevent the corruption of data that leads to total loss of data. Having data backup is the best way to distribute the risk of losing data. Research on ways that can be used to protect data from viruses and errors should be done to ensure that data is preserved and stored without the risk of loss. On data restoration and recovery, there are data filling algorithms that are essential in ensuring that data is not completely lost. A modification of these features would help in providing delete options before data is erased. Organizations need to upgrade systems and software before they get to the point of becoming unsteady due to age. Organizations should monitor data movement to identify patterns, determine the sensitive data, and define policies that should protect access to the data. Literature Review Summary Data loss has been happening since computer systems initially began showing up in working environments and started to notably rise during the 1980s. For instance, in 1984, the organization presently known as Experian had 90 million records lost in an attack. The objective of this study is to obviously see how data loss happens, its impacts, with the end goal that data loss can be easily stopped. Talesh (2018) stated that employees are the primary reason behind the cause of majority of the data loss. The most effective solution to data loss is a good data backup system, as it upsurges the probabilities of data restoration. Data restoration is mostly done by specialized firms that inspect hardware storage to recover deleted information and the effort to restore corrupted data through specialized procedures. According to Tran and Park (2020), data restoration is of prime significance. It solves the problem of data loss in organizations. Data restoration helps an organization resume its business operations without experiencing challenges (Tran & Park, 2020). Data backups are important strategies for ensuring business continuity. Tran &Park (2020) state that flash memory is one of the devices that could be used to restore data, and as a result, it overcomes the physical limitations of data loss and improves performance. Methodology Introduction Research strategies denote the instruments that one uses to conduct a research study. These can either be qualitative, quantitative, or blended. Surveys incorporate collecting data from large groups of people or companies by means of questionnaires, telephones, and interviews. Indeed, study exploration might be the main methodology in brain science where arbitrary testing is frequently used. Surveys can be long or short. They can be directed face to face, by phone calls, through the mail, or over the Internet social platforms. Despite the fact that overview data are regularly investigated utilizing insights, there are many inquiries that loan themselves to more qualitative research. The rationale for the Research Approach In this research, the researcher used a mixed-method approach as it dove-tailed both qualitative and quantitative approaches to identify and analyze the impact of data loss and restoration on various business organizations. Qualitative analysis is a strategy that collects data using conversational strategies, generally open-ended questions. The qualitative analysis includes gathering and investigating theoretical information (Dewasiri, Weerakoon & Azeez, 2018). Participants will be presented with questionnaires where they will theoretically answer the presented questions. The reactions gathered are basically non-mathematical. This strategy assists an analyst with getting what members think and why they think with a certain goal in mind. Quantitative strategies examine mathematical information and regularly necessitate the application of factual devices to examine collected data. This reflects the approximation of factors and influences between them. The data obtained from the survey can be presented in diagrams and tables. Unit Analysis The population of the study comprised of participants selected randomly from different business organizations. Random sampling is used to select the participants from their specific organizations (Etikan & Bala, 2017). A name is chosen from a list of names of employees in each organization, and the chosen employee is given a chance to participate in the survey. Trustworthiness Various steps have to be followed to ensure the accuracy, credibility, and believability of the research. There should be the use of recording gadgets and well-detailed field notes, which will improve the dependability of the research (Cohen, Manion & Morrison, 2017). The survey agents ought to likewise act naturally basic. The outcome should also be an exact understanding of the member’s initial data. The research method should be of high value and targeted to ensure exactly what I need to measure. An increase of randomization during sample selection will also decrease sample bias. Reliability One of the key requirements of any research is the reliability of information and findings. In order to ensure research reliability, I will use various methods such as interviews, questionnaires, and classroom observation to gather information. Gathering different types of data from various sources will improve the reliability of my research. The Calculation of internal consistency, for instance, through having two dissimilar questions that have a similar focus, will also increase the reliability of my research. The use of control groups will increase the effectiveness of the research results. Summary Research strategies denote the instruments that one uses to conduct a research study. These can either be qualitative, quantitative, or blended. In my research, I will use surveying to collect data on the impacts of data loss and restoration. Surveys incorporate collecting data from a large group of people or companies by means of questionnaires, telephones, and interviews. In this research, quantitative and qualitative methods will be used to research on impacts of data loss and restoration on various business organizations. Qualitative analysis is a strategy that gathers information utilizing conversational strategies, generally open-ended questions. Quantitative strategies examine mathematical information and regularly necessitates the application of factual devices to examine collected data. This reflects the approximation of factors and influences between them. The population of the study comprised of participants selected randomly from different business organizations. Random sampling is used to select the participants from their specific organizations. There should be the use of recording gadgets and well-detailed field notes, which will improve the dependability of the qualitative research. The research method should be of high value and targeted to ensure exactly what I need to measure. An increase of randomization during sample selection will also decrease sample bias. References Hsia, K. L. (2021). The Day the Computers Went Down. Annals of Surgery, 273(4), e138. ONU, F. U., & AKIENE, P. T. Data Loss Control In A Congested Network Using Computer Based Forecasting Techniques. Palkó, A., & Sujbert, L. (2021, May). Adaptive Fourier Analysis in the Case of Data Loss. In 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE. Talesh, S. A. (2018). Data breach, privacy, and cyber insurance: How insurance companies act as “compliance managers” for businesses. Law & Social Inquiry, 43(2), 417-440. Taylor, A. R. E. (2021). Standing by for data loss: Failure, preparedness and the cloud. ephemera: theory & politics in organization, 21(1). Tran, V. D., & Park, D. J. (2020). A survey of data recovery on flash memory. International Journal of Electrical & Computer Engineering (2088-8708), 10(1). Zeno-Zencovich, V. (2018). Liability for data loss. In Research Handbook in Data Science and Law. Edward Elgar Publishing.
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